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Robust iterative learning control for iteration- and time-varying disturbance rejection

Author

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  • Chengyuan Tan
  • Sen Wang
  • Jing Wang

Abstract

Iterative learning control (ILC) is an effective strategy to deal with repetitive tasks and has been widely applied in industrial systems. Many methods have been proposed to improving the performance of ILC system against iteration-invariant disturbances. While iteration-varying disturbances, which has more practical meaning, do not get enough researches. An observer is designed to estimate the system states and the total disturbances which include the system uncertainties and external disturbances. Furthermore, an iterative algorithm is given to estimate separately the disturbances from input and non-input channels. Then robust D-type ILC with disturbances compensation is proposed to improve the performance of systems with iteration-varying and time-varying disturbances. The convergence of proposed robust ILC system is proved, and the control parameters design is guided. Finally, simulation and comparison with other method are carried out to demonstrate the efficiency of the proposed method.

Suggested Citation

  • Chengyuan Tan & Sen Wang & Jing Wang, 2020. "Robust iterative learning control for iteration- and time-varying disturbance rejection," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(3), pages 461-472, February.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:3:p:461-472
    DOI: 10.1080/00207721.2020.1716103
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